# kokao_engine_v10_0/kokao_ray.py
"""KokaoRay — Горизонтальное масштабирование через Ray."""

import os
import sys

sys.path.insert(
    0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "kokao-v9")
)

try:
    import ray

    HAS_RAY = True
except ImportError:
    HAS_RAY = False

    def remote(cls):
        """Декоратор для создания remote-класса (mock)."""

        class RemoteWrapper:
            @staticmethod
            def remote(*args, **kwargs):
                """Создание экземпляра actor с обёрткой методов."""
                instance = cls(*args, **kwargs)

                # Обёртываем методы для имитации .remote()
                class ActorProxy:
                    def __init__(self, obj):
                        self._obj = obj

                    def __getattr__(self, name):
                        attr = getattr(self._obj, name)
                        if callable(attr):
                            # Возвращаем обёртку со статическим .remote()
                            def remote_call(*a, **kw):
                                return attr(*a, **kw)

                            return type("RemoteMethod", (), {"remote": staticmethod(remote_call)})()
                        return attr

                return ActorProxy(instance)

        RemoteWrapper.__name__ = cls.__name__
        return RemoteWrapper

    class MockRay:
        """Mock Ray для тестов без установки."""

        @staticmethod
        def get(futures):
            """Получить результат."""
            if isinstance(futures, list):
                return [f.result() if hasattr(f, "result") else f for f in futures]
            return futures.result() if hasattr(futures, "result") else futures

        @staticmethod
        def init(*args, **kwargs):
            pass

        @staticmethod
        def is_initialized():
            return False

    ray = MockRay()
    ray.remote = remote

import torch

from .core import KOKAOEngine


@ray.remote
class KokaoRayActor:
    """Ray Actor для распределённого обучения.

    Federated Learning: агрегация весов через протокол
    """

    def __init__(self, n_features: int):
        self.engine = KOKAOEngine(n_features=n_features)

    def train(self, x_batch: list, target_batch: list) -> bytes:
        """Обучение на локальном батче."""
        for x, target in zip(x_batch, target_batch):
            # Конвертируем список в tensor
            x_tensor = torch.tensor(x, device=self.engine.device)
            self.engine.solve_level2(x_tensor, target)
        # Сериализация весов
        import pickle

        return pickle.dumps(self.engine.core.w.tolist())

    def get_weights(self) -> list:
        """Получить веса."""
        return self.engine.core.w.tolist()


class KokaoRayCluster:
    """Кластер Ray Actors для распределённого обучения.

    Преимущества: петабайты данных, гранты на Big Data
    """

    def __init__(self, n_actors: int = 2, n_features: int = 10):
        if HAS_RAY:
            ray.init(ignore_reinit_error=True)
        self.actors = [KokaoRayActor.remote(n_features) for _ in range(n_actors)]
        self.n_features = n_features

    def train(self, x_batches: list, target_batches: list) -> None:
        """Распределённое обучение на всех actors."""
        futures = [
            actor.train.remote(x, t) for actor, x, t in zip(self.actors, x_batches, target_batches)
        ]
        weights_bytes = ray.get(futures)
        # Federated aggregation (упрощённо)
        self.aggregated_weights = self._aggregate(weights_bytes)

    def _aggregate(self, weights_bytes_list: list) -> list:
        """Агрегация весов (Federated Averaging)."""
        import pickle

        weights_list = [pickle.loads(wb) for wb in weights_bytes_list]
        # Усреднение
        avg_weights = [
            sum(w[i] for w in weights_list) / len(weights_list) for i in range(len(weights_list[0]))
        ]
        return avg_weights
